CN115900838A - Slope early warning method and system, computer equipment and readable storage medium - Google Patents
Slope early warning method and system, computer equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a slope early warning method, a system, computer equipment and a readable storage medium, wherein the method comprises the following steps: acquiring rainfall data and deformation data of a target area; analyzing the deformation data, identifying and screening abnormal data, and judging whether the abnormal data is caused by real change of the rock-soil body; if not, optimizing abnormal data, and performing periodic correlation analysis on the optimized deformation data and rainfall data; and when the deformation data and the rainfall data reach the corresponding early warning threshold value at the same acquisition moment, triggering an early warning mechanism. By considering the coupling effect of rainfall and rock-soil bodies and fusing monitoring items such as cracks, inclination angles and acceleration, periodic correlation analysis is carried out on deformation data and rainfall data, and only when all data reach corresponding early warning threshold values at the same acquisition moment, an early warning mechanism is triggered, so that the generation of false alarm of a single criterion is avoided, and the accuracy of monitoring and early warning is further improved.
Description
Technical Field
The invention relates to the technical field of slope monitoring, in particular to a slope early warning method, a slope early warning system, computer equipment and a readable storage medium.
Background
The landslide is a natural phenomenon that soil or rock mass on a slope is influenced by factors such as river erosion, underground water movement, rainwater immersion, earthquake and artificial slope cutting, slides downwards along the slope integrally or dispersedly along a certain weak surface or a weak zone under the action of gravity, and the threat of the landslide to the life and property of people can be reduced by monitoring environmental parameters at the slope and analyzing based on monitoring data.
In the prior art, monitoring items such as displacement and cracks of a side slope are monitored and early warned in real time, and the accuracy of an early warning mechanism is easily influenced by abnormal data caused by unreal change of monitoring points due to complex and changeable environment of the side slope; or the water content is used as a single monitoring item for monitoring, the water content is used as a control parameter of the side slope shear strength, and the early warning is carried out by analyzing the change characteristics of the water content of the side slope and the shear strength along with the rainfall time.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a slope early warning method, a slope early warning system, computer equipment and a readable storage medium, and aims to solve the technical problem that the slope monitoring early warning is poor in accuracy in the prior art.
In order to realize the purpose, the invention is realized by the following technical scheme: a slope early warning method comprises the following steps:
acquiring rainfall data of a target area and deformation data of monitoring points of rock and soil bodies in the target area, wherein the deformation data comprises inclination angle data, crack data and acceleration data, and the rainfall data comprises rainfall data and accumulated rainfall data;
analyzing the deformation data to identify and screen abnormal data with abnormal fluctuation, and judging whether the abnormal data is caused by real change of the rock-soil body;
if the abnormal data is caused by unreal changes of the rock-soil body, optimizing the deformation data to eliminate abnormal fluctuation, and performing periodic correlation analysis on the optimized deformation data and the optimized rainfall data;
and triggering an early warning mechanism when the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data all reach corresponding early warning thresholds at the same acquisition moment.
Compared with the prior art, the invention has the beneficial effects that: the rainfall data and the deformation data of the rock-soil body in the target area are obtained, the deformation data are analyzed before data analysis, abnormal data with abnormal fluctuation are identified and screened out, whether the abnormal data are caused by real change of the rock-soil body or not is judged, if not, the abnormal data are optimized, the influence of abnormal data caused by non-real change on a subsequent early warning mechanism is prevented, and early warning accuracy is improved; further, by considering the coupling effect of rainfall and a rock-soil body, combining monitoring items such as cracks, inclination angles and acceleration, carrying out periodic correlation analysis on the optimized deformation data and rainfall data, setting early warning thresholds respectively for each item of data, and triggering an early warning mechanism only when all the data reach the corresponding early warning thresholds at the same acquisition time, so that the situation that all the data reach the corresponding thresholds at the same acquisition time is convenient to understand, namely, all the monitoring data show the same change trend and reach the early warning values within a period of time, the error of a single criterion is prevented from generating false warning, and the accuracy of monitoring and early warning is further improved.
According to an aspect of the foregoing technical solution, the step of analyzing the deformation data to identify and screen out abnormal data having abnormal fluctuations specifically includes:
acquiring a first monitoring data pair acquired by the monitoring point at the current acquisition time, wherein the first monitoring data pair comprises two continuously acquired monitoring data, and the two monitoring data in the first monitoring data pair are one of the inclination angle data, the crack data and the acceleration data;
and when the difference value of the two monitoring data in the first monitoring data pair exceeds a first preset threshold value, judging that the monitoring data are abnormal data.
According to one aspect of the above technical solution, the step of determining whether the abnormal data is caused by real change of the rock-soil mass specifically includes:
respectively acquiring a second monitoring data pair and a third monitoring data pair corresponding to the first monitoring data pair at the current acquisition time, wherein the second monitoring data pair comprises two continuously acquired monitoring data, the two monitoring data in the second monitoring data pair are both one of the inclination angle data, the crack data and the acceleration data, the third monitoring data pair comprises two continuously acquired monitoring data, the two monitoring data in the third monitoring data pair are both one of the inclination angle data, the crack data and the acceleration data, and the first monitoring data pair, the second monitoring data pair and the third monitoring data pair are respectively one of the inclination angle data, the crack data and the acceleration data;
respectively judging whether the difference value of the two monitoring data in the second monitoring data pair exceeds a second preset threshold value or not, and whether the difference value of the two monitoring data in the third monitoring data pair exceeds a third preset threshold value or not;
and if the difference value corresponding to the second monitoring data pair and/or the third monitoring data pair does not exceed a second preset threshold value and/or a third preset threshold value, judging that the abnormal data is caused by unreal changes of the rock-soil body.
According to an aspect of the foregoing technical solution, the step of performing optimization processing on the deformation data to eliminate abnormal fluctuation specifically includes:
screening an abnormal data group from the deformed data, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and reordering the data in the abnormal data group according to the numerical value;
if the number of the data is even, obtaining and outputting the average value of the middle two data;
and if the number of the data is odd, acquiring intermediate data and outputting the intermediate data.
According to an aspect of the foregoing technical solution, the step of performing optimization processing on the deformation data to eliminate abnormal fluctuation specifically includes:
setting fluctuation amplitude limit between two adjacent data;
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, calculating the difference value between the current-time acquired data and the previous-time acquired data in the abnormal data group, and judging whether the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit or not;
and if the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit, replacing the acquired numerical value of the acquired data at the current moment with the acquired numerical value of the acquired data at the previous moment.
According to an aspect of the foregoing technical solution, the step of performing optimization processing on the deformation data to eliminate abnormal fluctuation specifically includes:
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and performing moving average processing on the abnormal data group to eliminate abnormal fluctuation.
According to one aspect of the above technical solution, the step of performing periodic correlation analysis on the optimized deformation data and rainfall data specifically includes:
setting an x coordinate as time and a y coordinate as parameter values, and drawing corresponding curve parameter graphs respectively according to the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data;
unifying the y coordinates of the curve parameter graphs based on the incidence angle data, the crack data, the acceleration data, the rainfall data and the correlation information among the accumulated rainfall, and summarizing the curve parameters of all the data to form a monitoring correlation data table.
On the other hand, the invention also provides a slope early warning system, which comprises:
the monitoring module is used for acquiring rainfall data of a target area and deformation data of monitoring points of rock and soil bodies in the target area, wherein the deformation data comprises inclination angle data, crack data and acceleration data, and the rainfall data comprises rainfall data and accumulated rainfall data;
the abnormal module is used for analyzing the deformation data to identify and screen abnormal data with abnormal fluctuation and judge whether the abnormal data is caused by real change of the rock-soil body;
the correlation module is used for optimizing the deformation data to eliminate abnormal fluctuation and carrying out periodic correlation analysis on the optimized deformation data and the optimized rainfall data;
and the early warning module is used for respectively setting early warning thresholds for the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data, and triggering an early warning mechanism when the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data all reach the early warning thresholds at the same acquisition moment.
According to an aspect of the foregoing technical solution, the exception module specifically includes:
the first acquisition unit is used for acquiring a first monitoring data pair acquired by the monitoring point at the current acquisition moment, wherein the first monitoring data pair comprises two continuously acquired monitoring data, and the two monitoring data in the first monitoring data pair are one of the inclination angle data, the crack data and the acceleration data;
and the first judgment unit is used for judging that the monitoring data are abnormal data when the difference value of the two monitoring data in the first monitoring data pair exceeds a first preset threshold value.
According to an aspect of the foregoing technical solution, the exception module further includes:
a second obtaining unit, configured to obtain a second monitoring data pair and a third monitoring data pair corresponding to the first monitoring data pair at the current collection time, respectively, where the second monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the second monitoring data pair are both one of the inclination data, the crack data, and the acceleration data, the third monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the third monitoring data pair are both one of the inclination data, the crack data, and the acceleration data, and the first monitoring data pair, the second monitoring data pair, and the third monitoring data pair are each one of the inclination data, the crack data, and the acceleration data;
a judging unit, configured to respectively judge whether a difference between two monitoring data in the second monitoring data pair exceeds a second preset threshold, and whether a difference between two monitoring data in the third monitoring data pair exceeds a third preset threshold;
and the second judging unit is used for judging that the abnormal data is caused by unreal changes of the rock-soil body if the difference value corresponding to the second monitoring data pair and/or the third monitoring data pair does not exceed a second preset threshold value and/or a third preset threshold value.
According to an aspect of the foregoing technical solution, the association module specifically includes:
the first optimization unit is used for screening an abnormal data group from the deformation data, the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and the data in the abnormal data group is reordered according to the value;
if the number of the data is an even number, obtaining and outputting the average value of the two middle data;
and if the number of the data is odd, acquiring and outputting intermediate data.
According to an aspect of the foregoing technical solution, the association module specifically includes:
the second optimization unit is used for setting fluctuation amplitude limit between two adjacent data;
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, calculating the difference value between the current-time acquired data and the previous-time acquired data in the abnormal data group, and judging whether the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit or not;
and if the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit, replacing the acquired numerical value of the acquired data at the current moment with the acquired numerical value of the acquired data at the previous moment.
According to an aspect of the foregoing technical solution, the association module specifically includes:
and the third optimization unit is used for acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and the abnormal data group is subjected to moving average processing to eliminate abnormal fluctuation.
According to an aspect of the foregoing technical solution, the association module specifically includes:
the correlation analysis unit is used for setting an x coordinate as time and a y coordinate as a parameter value, and respectively drawing corresponding curve parameter graphs according to the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data;
unifying the y coordinates of the curve parameter graphs based on the incidence angle data, the crack data, the acceleration data, the rainfall data and the correlation information among the accumulated rainfall, and summarizing the curve parameters of all the data to form a monitoring correlation data table.
In another aspect, the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the slope warning method according to the above technical solution when executing the computer program.
On the other hand, the invention also provides a readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the slope early warning method in the above technical solution is implemented.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a slope early warning method according to a first embodiment of the invention;
FIG. 2 is a graph of rainfall data curve parameters according to a first embodiment of the present invention;
FIG. 3 is a graph of curve parameters of deformation data according to a first embodiment of the present invention;
FIG. 4 is a monitoring correlation data table according to a first embodiment of the present invention;
fig. 5 is a structural block diagram of a slope early warning system in a second embodiment of the invention;
fig. 6 is a schematic structural diagram of a data processing apparatus according to a fourth embodiment of the present invention.
The main components in the figure are illustrated by symbols:
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Various embodiments of the present invention are presented in the figures. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1, a flowchart of a slope warning method according to a first embodiment of the present invention is shown, including the following steps:
step S110, acquiring rainfall data of a target area and deformation data of monitoring points of rock and soil bodies in the target area, wherein the deformation data comprises inclination angle data, crack data and acceleration data, and the rainfall data comprises rainfall data and accumulated rainfall data. Specifically, in this embodiment, the rainfall data and the accumulated rainfall data are obtained by monitoring by arranging a rain gauge at a landslide platform of the target area, and the deformation data is obtained by monitoring by arranging equipment such as an inclinometer, a crack gauge and an accelerometer at a crack of the target area.
In some application scenarios of this embodiment, monitoring instrument devices are arranged on the landslide of the target area according to grids, 2 stay wire displacement meters are arranged at the crack of the rear edge of the landslide, 3 tilt angle sensors are arranged at the front edge of the landslide, and 3 laser ranging sensors are arranged at the front edge of the landslide, which mainly monitor deformation conditions such as displacement, crack, tilt angle, acceleration and the like of the landslide. Preferably, in this embodiment, 4 soil moisture content meters are further distributed in the middle of the landslide, and moisture content changes before, during, and after rain can be directly compared based on the moisture content meters for monitoring moisture content at different burial depth positions in real time.
And step S120, analyzing the deformation data to identify and screen abnormal data with abnormal fluctuation, and judging whether the abnormal data is caused by real change of the rock-soil body.
Preferably, in this embodiment, the step S120 specifically includes:
step S121, acquiring a first monitoring data pair acquired by the monitoring point at the current acquisition time, wherein the first monitoring data pair comprises two continuously acquired monitoring data, and the two monitoring data in the first monitoring data pair are one of the inclination angle data, the crack data and the acceleration data;
and when the difference value of the two monitoring data in the first monitoring data pair exceeds a first preset threshold value, judging that the monitoring data are abnormal data. Specifically, when two consecutive data change greatly, it is indicated that the data are abnormal, and it may be that the environment of the target area changes abnormally to cause large data fluctuation, or that the sensor data fails to cause data abnormality.
Further, in this embodiment, the step S120 further includes:
step S122, respectively obtaining a second monitoring data pair and a third monitoring data pair corresponding to the first monitoring data pair at the current collection time, where the second monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the second monitoring data pair are both one of the inclination data, the crack data and the acceleration data, the third monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the third monitoring data pair are both one of the inclination data, the crack data and the acceleration data, and the first monitoring data pair, the second monitoring data pair and the third monitoring data pair are respectively one of the inclination data, the crack data and the acceleration data;
respectively judging whether the difference value of the two monitoring data in the second monitoring data pair exceeds a second preset threshold value or not, and whether the difference value of the two monitoring data in the third monitoring data pair exceeds a third preset threshold value or not;
and if the difference value corresponding to the second monitoring data pair and/or the third monitoring data pair does not exceed a second preset threshold value and/or a third preset threshold value, judging that the abnormal data is caused by unreal changes of the rock-soil body.
Specifically, the deformation data is formed by real-time synchronous monitoring of the inclination angle data, the crack data, the acceleration data and the like, when any one of the data fluctuates, whether the data is an accidental abnormality can be judged based on the related change of other data, and when only one or two of the deformation data changes, the abnormal data can be judged to be caused by unreal change of the rock-soil body.
And S130, if the abnormal data is caused by unreal changes of the rock-soil body, optimizing the deformation data to eliminate abnormal fluctuation, and performing periodic correlation analysis on the optimized deformation data and the optimized rainfall data.
Specifically, in order to facilitate the subsequent correlation analysis of the monitoring data and ensure the accuracy of the early warning, preferably, in this embodiment, for the optimization processing of the abnormal data, the step S130 specifically includes:
s131, screening an abnormal data group from the deformed data, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and reordering the data in the abnormal data group according to the numerical value;
if the number of the data is an even number, obtaining and outputting the average value of the two middle data;
and if the number of the data is odd, acquiring intermediate data and outputting the intermediate data.
Specifically, when abnormal data is identified, an abnormal data group is formed by acquiring monitoring data before the abnormal data, and a method for taking a median value by arranging data is adopted, so that the method has a good effect on a slowly-changing measured value, can overcome the change caused by accidental factors, and can effectively remove abnormal burrs of sensor data.
Preferably, in this embodiment, for the optimization processing of the abnormal data, the step S130 specifically includes:
step S132, setting the fluctuation amplitude limit between two adjacent data;
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, calculating the difference value between the current acquisition data and the previous acquisition data in the abnormal data group, and judging whether the absolute value of the difference value of the two acquisition data is greater than the fluctuation amplitude limit or not;
and if the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit, replacing the acquired numerical value of the acquired data at the current moment with the acquired numerical value of the acquired data at the previous moment.
Specifically, the absolute value x of the difference and the amplitude y are determined by subtracting the current data from the previous data. If x is greater than y, the current data is replaced by the previous data, otherwise, the data is not processed, the mode is equivalent to amplitude limiting of the data, the mode also has a good effect on the slowly-changing measured value, can overcome the change caused by accidental factors, ensures the stability of the data, and improves the early warning accuracy.
Preferably, in this embodiment, for the optimization processing of the abnormal data, the step S130 specifically includes:
step S133, acquiring an abnormal data group, where the abnormal data group includes a plurality of abnormal data and non-abnormal data arranged according to the acquisition time, and performing a moving average process on the abnormal data group to eliminate abnormal fluctuation.
Specifically, in this step, the above-mentioned moving average processing is to output the average value of a plurality of data within the window, and this method has a good effect of suppressing the periodic interference.
Further, in this embodiment, for the identification of the abnormal data in step S120, in addition to the difference judgment of the fluctuation of two consecutive data, that is, the occurrence of a burr in the data, the difference judgment of the acquisition time of two consecutive monitoring data is also performed, that is, when the difference between the adjacent acquisition times of the same measurement point exceeds a set threshold, it indicates that the sensor may have data interruption, and after the abnormality is determined, a fault alarm is performed, and software and hardware troubleshooting and field troubleshooting are performed manually, so as to prevent the misjudgment of the abnormal data due to the large fluctuation of the data caused by the failure of the sensing device, and to affect the subsequent judgment and early warning.
In addition, in this embodiment, for the identification of the abnormal data in step S120, besides the occurrence of a burr or an interruption of the data, the identification of a step jump of the data is also included, and by taking a sliding median of the first five pieces of data acquired from the current data acquired, starting from the current data acquired, if a difference between the six consecutive data acquired and the sliding median is greater than a threshold and slopes of fitted straight lines at the six points are less than a threshold K, the data is indicated to have the step jump, specific threshold parameters are set according to different factors such as sensor types, and when it is determined that the data has the step jump, a fault alarm is performed.
Further, in the present embodiment. In the above section of performing the periodic correlation analysis on the optimized deformation data and rainfall data, the step S130 specifically includes:
step S134, setting an x coordinate as time, setting a y coordinate as a parameter value, and drawing corresponding curve parameter graphs respectively according to the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data;
unifying the y coordinates of the curve parameter graphs based on the incidence angle data, the crack data, the acceleration data, the rainfall data and the correlation information among the accumulated rainfall, and summarizing the curve parameters of all the data to form a monitoring correlation data table.
Specifically, in the present embodiment, the rainfall data, that is, the curve parameter graph formed by the rainfall data and the accumulated rainfall data, is shown in fig. 2, the monitoring frequency can be adjusted based on the forecast of the weather station, when the target area has rainstorm weather, the slope can be monitored in an encrypted manner, the monitoring frequency is increased, as can be seen from the graph, the area starts to have rainstorm weather in 6 months and 16 days, and the geological disaster weather risk level of the area is updated to be red early warning in 0 minute at 6 months and 19 days and 20 days; the weather station changes to a rainstorm early warning grade to be red early warning in 15 days of 6 months and 20 days, the slope is encrypted and monitored, and the monitoring frequency is improved. As can be seen from the analysis of data collected by the rain gauge, the real-time rainfall increases sharply from 18 days in 6 months, reaches the maximum quantity in 20 days in 6 months, is about 317.7mm, and belongs to the super heavy rainstorm grade. As shown in fig. 3, it can be seen that, during the period from 19 days/6 months to 20 days/6 months, the monitoring item QJ01 (inclination angle) data has an abnormal trend, the inclination angle in the X direction changes from 1.09 to-1.48, and the variation is 2.57; the Y-direction inclination angle integrated value was changed from 2.4 to 3.01 by an amount of 0.61. The cumulative displacement at point LF02 (crack) suddenly increased from 249.8mm to 661.8mm at a change of 412mm. The JS03 (acceleration) gX component value changed from-0.5 mg to-34.1 mg, which was 33.6mg; the gX component value varied from-2.1 mg to 9.1mg, with a variation of 11.2mg. The monitoring association data table is shown in fig. 4, and the y coordinates of the parameter data are unified, so that monitoring and observation can be facilitated, and early warning threshold values can be conveniently and uniformly set for subsequent cells based on the y coordinates.
Step S140, when the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data all reach corresponding early warning thresholds at the same acquisition moment, triggering an early warning mechanism. Specifically, in this step, the early warning threshold of each data may be set individually as required.
In summary, in the slope early warning method in the above embodiment of the present invention, the rainfall data and the deformation data of the rock-soil body in the target area are obtained, the deformation data is analyzed before data analysis, abnormal data with abnormal fluctuation is identified and screened out, whether the abnormal data is caused by real change of the rock-soil body is determined, if not, the abnormal data is optimized, a subsequent early warning mechanism is prevented from being affected by the abnormal data caused by non-real change, and the early warning accuracy is improved; further, the deformation data and the rainfall data after the optimization processing are subjected to periodic correlation analysis, early warning thresholds are respectively set for all data, and only when all the data reach the corresponding early warning thresholds at the same acquisition time, an early warning mechanism is triggered, so that the situation is convenient to understand, and when all the data reach the corresponding thresholds at the same acquisition time, namely, all the monitored data show the same change trend and reach the early warning values within a period of time, the error of a single criterion is avoided from generating false alarm, the accuracy of the slope early warning mechanism is improved, the slope stability is comprehensively analyzed by considering the coupling effect of the rainfall and the rock-soil body and fusing monitoring items such as cracks, inclination angles, acceleration, displacement and the like, the stability of the slope is more comprehensively evaluated, the monitored data are optimized and integrated, and the abnormal data of unreal changes of monitoring points are analyzed through the preprocessing, so that the authenticity of the early warning is more favorably distinguished and analyzed.
The second embodiment of the present invention also provides a slope early warning system, as shown in fig. 5, the slope early warning system includes:
the monitoring module 100 is configured to acquire rainfall data of a target area and deformation data of monitoring points of a rock body in the target area, where the deformation data includes inclination angle data, crack data and acceleration data, and the rainfall data includes rainfall data and accumulated rainfall data;
an abnormal module 200, configured to analyze the deformation data to identify and screen out abnormal data with abnormal fluctuation, and determine whether the abnormal data is caused by real change of the rock-soil body;
the correlation module 300 is configured to perform optimization processing on the deformation data to eliminate abnormal fluctuation, and perform periodic correlation analysis on the optimized deformation data and the optimized rainfall data;
the early warning module 400 is configured to set early warning thresholds for the inclination angle data, the crack data, the acceleration data, the rainfall data, and the accumulated rainfall data, respectively, and trigger an early warning mechanism when the inclination angle data, the crack data, the acceleration data, the rainfall data, and the accumulated rainfall data all reach the early warning thresholds at the same acquisition time.
Preferably, in this embodiment, the exception module 200 specifically includes:
the first acquisition unit is used for acquiring a first monitoring data pair acquired by the monitoring point at the current acquisition moment, wherein the first monitoring data pair comprises two continuously acquired monitoring data, and the two monitoring data in the first monitoring data pair are one of the inclination angle data, the crack data and the acceleration data;
and the first judging unit is used for judging the monitoring data as abnormal data when the difference value of the two monitoring data in the first monitoring data pair exceeds a first preset threshold value.
Preferably, in this embodiment, the exception module 200 specifically includes: a second obtaining unit, configured to obtain a second monitoring data pair and a third monitoring data pair corresponding to the first monitoring data pair at the current collection time, respectively, where the second monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the second monitoring data pair are both one of the inclination data, the crack data, and the acceleration data, the third monitoring data pair includes two continuously collected monitoring data, the two monitoring data in the third monitoring data pair are both one of the inclination data, the crack data, and the acceleration data, and the first monitoring data pair, the second monitoring data pair, and the third monitoring data pair are each one of the inclination data, the crack data, and the acceleration data;
a judging unit, configured to respectively judge whether a difference between two monitoring data in the second monitoring data pair exceeds a second preset threshold, and whether a difference between two monitoring data in the third monitoring data pair exceeds a third preset threshold;
and the second judging unit is used for judging that the abnormal data is caused by the unreal change of the rock-soil body if the difference value corresponding to the second monitoring data pair and/or the third monitoring data pair does not exceed a second preset threshold value and/or a third preset threshold value.
Preferably, in this embodiment, the association module 300 specifically includes:
the first optimization unit is used for screening an abnormal data group from the deformation data, the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and the data in the abnormal data group is reordered according to the value;
if the number of the data is even, obtaining and outputting the average value of the middle two data;
and if the number of the data is odd, acquiring intermediate data and outputting the intermediate data.
Preferably, in this embodiment, the association module 300 specifically includes:
the second optimization unit is used for setting fluctuation amplitude limit between two adjacent data;
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, calculating the difference value between the current acquisition data and the previous acquisition data in the abnormal data group, and judging whether the absolute value of the difference value of the two acquisition data is greater than the fluctuation amplitude limit or not;
and if the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit, replacing the acquired numerical value of the acquired data at the current moment with the acquired numerical value of the acquired data at the previous moment.
Preferably, in this embodiment, the association module 300 specifically includes:
and the third optimization unit is used for acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and the abnormal data group is subjected to moving average processing to eliminate abnormal fluctuation.
Preferably, in this embodiment, the association module 300 specifically includes:
the correlation analysis unit is used for setting an x coordinate as time and a y coordinate as a parameter value, and respectively drawing corresponding curve parameter graphs according to the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data;
unifying the y coordinates of the curve parameter graphs based on the incidence angle data, the crack data, the acceleration data, the rainfall data and the correlation information among the accumulated rainfall, and summarizing the curve parameters of all the data to form a monitoring correlation data table.
In summary, in the slope early warning system in this embodiment, the monitoring module 100 is arranged to acquire rainfall data and deformation data of a rock-soil body in a target area, the abnormality module 200 is arranged to analyze the deformation data before data analysis, identify and screen out abnormal data with abnormal fluctuation, determine whether the abnormal data is caused by real change of the rock-soil body, and if not, optimize the abnormal data through the association module 300, so as to prevent a subsequent early warning mechanism from being affected by the abnormal data caused by the non-real change, and improve the early warning accuracy; further, the optimized deformation data and rainfall data are subjected to periodic correlation analysis, early warning thresholds are set for all data based on the early warning module 400, and an early warning mechanism is triggered only when all data reach corresponding early warning thresholds at the same acquisition time, so that the situation is convenient to understand.
A third embodiment of the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method as described above.
Referring to fig. 6, a data processing apparatus according to a fourth embodiment of the present invention includes a memory 20, a processor 10, and a computer program 30 stored in the memory and running on the processor, wherein the processor implements the method when executing the computer program.
The processor 10 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor or other data Processing chip in some embodiments, and is used for executing program codes stored in the memory 20 or Processing data, such as executing an access restriction program.
The memory 20 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 20 may in some embodiments be an internal storage unit of the data processing device, for example a hard disk of the data processing device. The memory 20 may also be an external storage device of the data processing apparatus in other embodiments, such as a plug-in hard disk provided on the data processing apparatus, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 20 may also include both an internal storage unit and an external storage device of the data processing apparatus. The memory 20 may be used not only to store application software installed in the data processing apparatus and various kinds of data, but also to temporarily store data that has been output or will be output.
It should be noted that the configuration shown in fig. 6 does not constitute a limitation of the data processing apparatus, which may comprise fewer or more components than shown, or some components may be combined, or a different arrangement of components in other embodiments.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A slope early warning method is characterized by comprising the following steps:
acquiring rainfall data of a target area and deformation data of monitoring points of a rock body in the target area, wherein the deformation data comprises inclination angle data, crack data and acceleration data, and the rainfall data comprises rainfall data and accumulated rainfall data;
analyzing the deformation data to identify and screen abnormal data with abnormal fluctuation, and judging whether the abnormal data is caused by real change of the rock-soil body;
if the abnormal data is caused by unreal changes of the rock-soil body, optimizing the deformation data to eliminate abnormal fluctuation, and performing periodic correlation analysis on the optimized deformation data and rainfall data;
and triggering an early warning mechanism when the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data all reach corresponding early warning thresholds at the same acquisition moment.
2. The slope early warning method according to claim 1, wherein the step of analyzing the deformation data to identify and screen out abnormal data with abnormal fluctuation specifically comprises:
acquiring a first monitoring data pair acquired by the monitoring point at the current acquisition time, wherein the first monitoring data pair comprises two continuously acquired monitoring data, and the two monitoring data in the first monitoring data pair are one of the inclination angle data, the crack data and the acceleration data;
and when the difference value of the two monitoring data in the first monitoring data pair exceeds a first preset threshold value, judging that the monitoring data are abnormal data.
3. The slope early warning method according to claim 2, wherein the step of determining whether the abnormal data is caused by real changes of the rock-soil mass specifically comprises:
respectively acquiring a second monitoring data pair and a third monitoring data pair corresponding to the first monitoring data pair at the current acquisition time, wherein the second monitoring data pair comprises two continuously acquired monitoring data, the two monitoring data in the second monitoring data pair are both one of the inclination angle data, the crack data and the acceleration data, the third monitoring data pair comprises two continuously acquired monitoring data, the two monitoring data in the third monitoring data pair are both one of the inclination angle data, the crack data and the acceleration data, and the first monitoring data pair, the second monitoring data pair and the third monitoring data pair are respectively one of the inclination angle data, the crack data and the acceleration data;
respectively judging whether the difference value of the two monitoring data in the second monitoring data pair exceeds a second preset threshold value or not, and whether the difference value of the two monitoring data in the third monitoring data pair exceeds a third preset threshold value or not;
and if the difference value corresponding to the second monitoring data pair and/or the third monitoring data pair does not exceed a second preset threshold value and/or a third preset threshold value, judging that the abnormal data is caused by unreal changes of the rock-soil body.
4. The slope pre-warning method according to claim 1, wherein the step of optimizing the deformation data to eliminate abnormal fluctuation specifically comprises:
screening an abnormal data group from the deformed data, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, and reordering the data in the abnormal data group according to the numerical value;
if the number of the data is even, obtaining and outputting the average value of the middle two data;
and if the number of the data is odd, acquiring and outputting intermediate data.
5. The slope pre-warning method according to claim 1, wherein the step of optimizing the deformation data to eliminate abnormal fluctuation specifically comprises:
setting fluctuation amplitude limit between two adjacent data;
acquiring an abnormal data group, wherein the abnormal data group comprises a plurality of abnormal data and non-abnormal data which are arranged according to the acquisition time, calculating the difference value between the current acquisition data and the previous acquisition data in the abnormal data group, and judging whether the absolute value of the difference value of the two acquisition data is greater than the fluctuation amplitude limit or not;
and if the absolute value of the difference value of the two acquired data is greater than the fluctuation amplitude limit, replacing the acquired numerical value of the acquired data at the current moment with the acquired numerical value of the acquired data at the previous moment.
6. The slope early warning method according to claim 1, wherein the step of optimizing the deformation data to eliminate abnormal fluctuations specifically comprises:
acquiring an abnormal data group which comprises a plurality of abnormal data and non-abnormal data arranged according to the acquisition time, and performing moving average processing on the abnormal data group to eliminate abnormal fluctuation.
7. The slope early warning method according to claim 1, wherein the step of performing periodic correlation analysis on the optimized deformation data and rainfall data specifically comprises:
setting an x coordinate as time and a y coordinate as parameter values, and drawing corresponding curve parameter graphs respectively according to the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data;
unifying the y coordinates of all curve parameter graphs based on the incidence angle data, the crack data, the acceleration data, the rainfall data and the correlation information among the accumulated rainfall, and summarizing the curve parameters of all data to form a monitoring correlation data table.
8. A side slope early warning system, characterized in that includes:
the monitoring module is used for acquiring rainfall data of a target area and deformation data of monitoring points of rock and soil bodies in the target area, wherein the deformation data comprises inclination angle data, crack data and acceleration data, and the rainfall data comprises rainfall data and accumulated rainfall data;
the abnormal module is used for analyzing the deformation data to identify and screen abnormal data with abnormal fluctuation and judge whether the abnormal data is caused by real change of the rock-soil body;
the correlation module is used for optimizing the deformation data to eliminate abnormal fluctuation and carrying out periodic correlation analysis on the optimized deformation data and the optimized rainfall data;
and the early warning module is used for setting early warning thresholds aiming at the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data respectively, and triggering an early warning mechanism when the inclination angle data, the crack data, the acceleration data, the rainfall data and the accumulated rainfall data all reach the early warning thresholds at the same acquisition moment.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the slope warning method according to any one of claims 1 to 7 when executing the computer program.
10. A readable storage medium on which a computer program is stored, which program, when executed by a processor, carries out the slope warning method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117423224A (en) * | 2023-09-27 | 2024-01-19 | 深圳市地质环境研究院有限公司 | Data acquisition method of slope monitoring internet of things equipment |
| CN117494483A (en) * | 2024-01-02 | 2024-02-02 | 中铁上海工程局集团第七工程有限公司 | Numerical optimization method for deformation data of double-hole tunnel section |
| CN117493833A (en) * | 2023-12-29 | 2024-02-02 | 江西飞尚科技有限公司 | Landslide deformation stage identification method, landslide deformation stage identification system, storage medium and computer |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205621237U (en) * | 2016-05-16 | 2016-10-05 | 西南科技大学 | Long -range automatic monitoring early warning system of slip mass that many states triggered |
| CN109785584A (en) * | 2019-01-29 | 2019-05-21 | 青岛理工大学 | Compound hydrodynamic force reservoir stability stability prediction method |
| CN112927478A (en) * | 2020-12-30 | 2021-06-08 | 广州翰南工程技术有限公司 | Geological disaster universal monitoring and early warning system |
| CN114322997A (en) * | 2021-11-30 | 2022-04-12 | 中煤科工集团沈阳设计研究院有限公司 | Strip mine side slope safety monitoring method |
| CN114333249A (en) * | 2022-01-14 | 2022-04-12 | 四川九洲北斗导航与位置服务有限公司 | Landslide early warning method and device |
| CN115035690A (en) * | 2021-03-05 | 2022-09-09 | 重庆市地质灾害防治中心 | Geological disaster monitoring and early warning method |
| CN115410342A (en) * | 2022-08-26 | 2022-11-29 | 安徽省地质矿产勘查局332地质队 | Landslide disaster intelligent early warning method based on crack meter real-time monitoring |
| CN115424415A (en) * | 2022-08-30 | 2022-12-02 | 安徽省新近纪防灾科技有限公司 | Landslide disaster intelligent early warning method based on GNSS real-time monitoring |
| CN115585767A (en) * | 2022-09-05 | 2023-01-10 | 中国地质环境监测院(自然资源部地质灾害技术指导中心) | Landslide early warning rule generation method based on multi-source monitoring data abnormal deformation mining |
-
2023
- 2023-03-10 CN CN202310225077.8A patent/CN115900838A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205621237U (en) * | 2016-05-16 | 2016-10-05 | 西南科技大学 | Long -range automatic monitoring early warning system of slip mass that many states triggered |
| CN109785584A (en) * | 2019-01-29 | 2019-05-21 | 青岛理工大学 | Compound hydrodynamic force reservoir stability stability prediction method |
| CN112927478A (en) * | 2020-12-30 | 2021-06-08 | 广州翰南工程技术有限公司 | Geological disaster universal monitoring and early warning system |
| CN115035690A (en) * | 2021-03-05 | 2022-09-09 | 重庆市地质灾害防治中心 | Geological disaster monitoring and early warning method |
| CN114322997A (en) * | 2021-11-30 | 2022-04-12 | 中煤科工集团沈阳设计研究院有限公司 | Strip mine side slope safety monitoring method |
| CN114333249A (en) * | 2022-01-14 | 2022-04-12 | 四川九洲北斗导航与位置服务有限公司 | Landslide early warning method and device |
| CN115410342A (en) * | 2022-08-26 | 2022-11-29 | 安徽省地质矿产勘查局332地质队 | Landslide disaster intelligent early warning method based on crack meter real-time monitoring |
| CN115424415A (en) * | 2022-08-30 | 2022-12-02 | 安徽省新近纪防灾科技有限公司 | Landslide disaster intelligent early warning method based on GNSS real-time monitoring |
| CN115585767A (en) * | 2022-09-05 | 2023-01-10 | 中国地质环境监测院(自然资源部地质灾害技术指导中心) | Landslide early warning rule generation method based on multi-source monitoring data abnormal deformation mining |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117423224A (en) * | 2023-09-27 | 2024-01-19 | 深圳市地质环境研究院有限公司 | Data acquisition method of slope monitoring internet of things equipment |
| CN117493833A (en) * | 2023-12-29 | 2024-02-02 | 江西飞尚科技有限公司 | Landslide deformation stage identification method, landslide deformation stage identification system, storage medium and computer |
| CN117493833B (en) * | 2023-12-29 | 2024-04-09 | 江西飞尚科技有限公司 | Landslide deformation stage identification method, landslide deformation stage identification system, storage medium and computer |
| CN117494483A (en) * | 2024-01-02 | 2024-02-02 | 中铁上海工程局集团第七工程有限公司 | Numerical optimization method for deformation data of double-hole tunnel section |
| CN117494483B (en) * | 2024-01-02 | 2024-03-19 | 中铁上海工程局集团第七工程有限公司 | Numerical optimization method for deformation data of double-hole tunnel section |
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